Improving the performance of a polygonal automobile exhaust thermoelectric generator with a crested porcupine optimizer

Applied Thermal Engineering, Год журнала: 2025, Номер unknown, С. 125946 - 125946

Опубликована: Фев. 1, 2025

Язык: Английский

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Hybrid prediction method for solar photovoltaic power generation using normal cloud parrot optimization algorithm integrated with extreme learning machine

Scientific Reports, Год журнала: 2025, Номер 15(1)

Опубликована: Фев. 22, 2025

As the energy crisis environmental concerns rise, harnessing renewable sources like photovoltaics (PV) is critical for sustainable development. However, seasonal variability and random intermittency of solar power pose significant forecasting challenges, threatening grid stability. Therefore, this paper proposes a novel hybrid method, NCPO-ELM, to adequately capture spatial temporal dependencies within meteorological data crucial accurate predictions. To effectively optimize performance Extreme Learning Machine (ELM), Normal Cloud Parrot Optimization (NCPO) algorithm developed, inspired by Pyrrhura Molinae parrots' flock behavior cloud model theory. NCPO integrates five unique search strategies utilizes structure explore exploit. By introducing normal generate samples with specific distributions, enhances solution space coverage. subsequently employed Single-Layer Feedforward Network (SLFN) hidden layer hyperparameters, yielding optimal weights biases output layer, thereby reducing benchmark ELM's sensitivity noise instability from initialization. The actual results PV stations across different regions demonstrate that proposed NCPO-ELM shows superior prediction accuracy compared existing approaches, particularly time series diverse characteristics variations.

Язык: Английский

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Using Crested Porcupine Optimizer Algorithm and CNN-LSTM-Attention Model Combined with Deep Learning Methods to Enhance Short-Term Power Forecasting in PV Generation

Energies, Год журнала: 2024, Номер 17(14), С. 3435 - 3435

Опубликована: Июль 12, 2024

Due to the inherent intermittency, variability, and randomness, photovoltaic (PV) power generation faces significant challenges in energy grid integration. To address these challenges, current research mainly focuses on developing more efficient management systems prediction technologies. Through optimizing scheduling integration PV generation, stability reliability of can be further improved. In this study, a new model is introduced that combines strengths convolutional neural networks (CNNs), long short-term memory (LSTM) networks, attention mechanisms, so we call algorithm CNN-LSTM-Attention (CLA). addition, Crested Porcupine Optimizer (CPO) utilized solve problem generation. This abbreviated as CPO-CLA. first time CPO has been into LSTM for parameter optimization. effectively capture univariate multivariate series patterns, multiple relevant target variables patterns (MRTPPs) are employed CPO-CLA model. The results show superior traditional methods recent popular models terms accuracy stability, especially 13 h timestep. mechanisms enables adaptively focus most historical data future prediction. optimizes network parameters, which ensures robust generalization ability great significance establishing trust market. Ultimately, it will help integrate renewable reliably efficiently.

Язык: Английский

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An improve crested porcupine algorithm for UAV delivery path planning in challenging environments

Scientific Reports, Год журнала: 2024, Номер 14(1)

Опубликована: Сен. 3, 2024

With the rapid advancement of drone technology and growing applications in field engineering, demand for precise efficient path planning complex dynamic environments has become increasingly important. Traditional algorithms struggle with terrain, obstacles, weather changes, often falling into local optima. This study introduces an Improved Crown Porcupine Optimizer (ICPO) planning, which enables drones to better avoid optimize flight paths, reduce energy consumption. Inspired by porcupines' defense mechanisms, a visuo-auditory synergy perspective is adopted, improving early convergence balancing visual auditory defenses. The also employs good point set population initialization strategy enhance diversity eliminates traditional reduction mechanism. To optima later stages, novel periodic retreat inspired defenses introduced position updates. Analysis on IEEE CEC2022 test shows that ICPO almost reaches optimal value, demonstrating robustness stability. In mountainous achieved values 778.1775 954.0118; urban 366.2789 910.1682 ranked first among compared algorithms, proving its effectiveness reliability delivery planning. Looking ahead, will provide greater efficiency safety navigating environments.

Язык: Английский

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Hierarchical RIME algorithm with multiple search preferences for extreme learning machine training

Alexandria Engineering Journal, Год журнала: 2024, Номер 110, С. 77 - 98

Опубликована: Окт. 7, 2024

Язык: Английский

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A novel optimization method: wave search algorithm

The Journal of Supercomputing, Год журнала: 2024, Номер 80(12), С. 16824 - 16859

Опубликована: Апрель 17, 2024

Язык: Английский

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Research on the Fiber-to-the-Room Network Traffic Prediction Method Based on Crested Porcupine Optimizer Optimization

Applied Sciences, Год журнала: 2024, Номер 14(11), С. 4840 - 4840

Опубликована: Июнь 3, 2024

In order to solve the problem of traffic burst due increase in access points and user movement an FTTR network, as well meet demand for a high-performance it is necessary rationally allocate network resources, accurate prediction very important dynamic bandwidth allocation such network. Therefore, this paper introduces novel model, named CPO-BiTCN-BiLSTM-SA, which integrates Crested Porcupine Optimizer (CPO), bidirectional temporal convolution (BiTCN), long short-term memory (BiLSTM) networks. BiTCN extends traditional TCN by incorporating data information, while BiLSTM enhances network’s capability learn from sequences. Moreover, self-attention (SA) mechanisms are utilized emphasize crucial segments data. Subsequently, BiTCN-BiLSTM-SA model optimized CPO obtain best hyperparameters, training performed achieve multi-step predictions based on single-step prediction. To evaluate model’s generalization ability, two distinct datasets employed Experimental findings demonstrate that proposed surpasses existing models terms root mean square error (RMSE), absolute (MAE), coefficient determination (R2). comparison with XGBoost has average reduction 29.50%, 25.43%, 25.00% RMSE, MAE, MAPE, respectively, 6.70% improvement R2.

Язык: Английский

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Parameters identification of photovoltaic models using Lambert W-function and Newton-Raphson method collaborated with AI-based optimization techniques: A comparative study

Expert Systems with Applications, Год журнала: 2024, Номер 255, С. 124777 - 124777

Опубликована: Июль 14, 2024

Accurately estimating the unknown parameters of photovoltaic (PV) models based on measured voltage-current data is a challenging optimization problem due to its high nonlinearity and multimodality. An accurate solution this essential for efficiently simulating, controlling, evaluating PV systems. There are three different models, including single-diode model, double-diode triple-diode with five, seven, nine parameters, respectively, proposed represent electrical characteristics systems varying levels complexity accuracy. In literature, several deterministic metaheuristic algorithms have been used accurately solve hard problem. However, problem, methods could not achieve solutions. On other side, algorithms, also known as gradient-free methods, somewhat good solutions but they still need further improvements strengthen their performance against stuck-in local optima slow convergence speed problems. Over last two years, recent better improve avoid tackle continuous majority those has investigated. Therefore, in paper, nineteen recently published such Mantis search algorithm (MSA), spider wasp optimizer (SWO), light spectrum (LSO), growth (GO), walrus (WAOA), hippopotamus (HOA), black-winged kite (BKA), quadratic interpolation (QIO), sinh cosh (SCHA), exponential distribution (EDO), optical microscope (OMA), secretary bird (SBOA), Parrot Optimizer (PO), Newton-Raphson-based (NRBO), crested porcupine (CPO), differentiated creative (DCS), propagation (PSA), one-to-one (OOBO), triangulation topology aggregation (TTAO), studied clarify effectiveness models. addition, collaborate functions, namely Lambert W-Function Newton-Raphson Method, aid solving I-V curve equations more accurately, thereby improving Those assessed using four well-known solar cells modules compared each metrics, best fitness, average worst standard deviation (SD), Friedman mean rank, speed; multiple-comparison test compare difference between ranks. Results comparison show that SWO efficient effective SDM, DDM, TDM over modules, Method equations. study reports perform poorly when applied

Язык: Английский

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DEMFFA: a multi-strategy modified Fennec Fox algorithm with mixed improved differential evolutionary variation strategies

Journal Of Big Data, Год журнала: 2024, Номер 11(1)

Опубликована: Май 8, 2024

Abstract The Fennec Fox algorithm (FFA) is a new meta-heuristic that primarily inspired by the fox's ability to dig and escape from wild predators. Compared with other classical algorithms, FFA shows strong competitiveness. “No free lunch” theorem an has different effects in face of problems, such as: when solving high-dimensional or more complex applications, there are challenges as easily falling into local optimal slow convergence speed. To solve this problem FFA, paper, improved Fenna fox DEMFFA proposed adding sin chaotic mapping, formula factor adjustment, Cauchy operator mutation, differential evolution mutation strategies. Firstly, mapping strategy added initialization stage make population distribution uniform, thus speeding up Secondly, order expedite speed algorithm, adjustments made factors whose position updated first stage, resulting faster convergence. Finally, prevent getting too early expand search space population, after second stages original update. In verify performance DEMFFA, qualitative analysis carried out on test sets, tested newly algorithms three sets. And we also CEC2020. addition, applied 10 practical engineering design problems 24-bar truss topology optimization problem, results show potential problems.

Язык: Английский

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Degradation prediction of PEMFC based on BiTCN-BiGRU-ELM fusion prognostic method

International Journal of Hydrogen Energy, Год журнала: 2024, Номер 87, С. 361 - 372

Опубликована: Сен. 9, 2024

Язык: Английский

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